Background

Model area proportions

Table 1: Area-proportions of the inshore and offshore zones and the thicknesses of the water column layers. The sea surface area of the model domain is an estimated 447888.734277354 km².

Table 2: Area proportions and other characteristics of the seabed habitat classes defined in the model by depth, rock or sediment type. The sea surface area of the model domain is an estimated 447888.734277354 km². Grain size is the median in mm, Permeability in units of m², nitrogen content in %dw.

Daily disturbance rates

[Copied from SBS, still needs compilation for Ascension?]

To calculate the natural disturbance rate, we needed depth-averaged current speeds (including tidal influence) and wave data. We used data products from Copernicus Marine Data; however, only surface tide components were available instead of depth-averaged values, and the depth-averaged current speeds did not include tides. Therefore, we approximated the influence of tides on the depth profile by assuming that the extra surface speed diminishes with depth at a certain decay rate. We employed a rough approximation for non-stratification and a flat bottom from the National Oceanography Centre (NOC). The equation assumes a constant speed in the upper half of the water column and attenuation in the lower half due to bottom friction, with speed decaying to zero at the bottom.

\[U_{} = U_{\text{current}} + \frac{U_{\text{tides }}}{1.07}\] \[V_{} = V_{\text{current}} + \frac{V_{\text{tides }}}{1.07}\]

We used the Copernicus toolbox for subsetting the specific variables, the SBS domain, and the desired period via Command Line Interface (CLI), then post-processed the data in R. Since the Copernicus Marine Data Store only hosts recent data for public download, we used data from 2022, the oldest full annual cycle available with all three variables. Details about the products used below.

Waves: We used the Global Ocean Waves Analysis product (DOI: 10.48670/moi-00017) with a 1/12-degree resolution. The parameters used every 3 hours were: significant wave height, mean wave direction, and primary swell wave period. Specific variables included Sea surface wave significant height (VHM0, m), Sea surface wave direction (VMDR, °), and Sea surface primary swell wave mean period (VTM01SW1, s).

Currents: We used the Global Ocean Physics Analysis and Forecast product (DOI: 10.48670/moi-00016) with a 1/12-degree resolution. The 6-hourly parameters used were Eastward sea water velocity (uo, m/s) and Northward sea water velocity (vo, m/s).

Tides: We used the Global Ocean Physics Analysis and Forecast product (DOI: 10.48670/moi-00016) with a 1/12-degree resolution. The 1-hourly parameters used were Surface sea water x velocity due to tide (utide, m/s) and Surface sea water y velocity due to tide (vtide, m/s).

Even though the grid is shared, there were coastal cells with currents but missing data for waves and tides. We interpolated missing coastal cells using the Voronoi tessellation method (nearest neighbor). We also interpolated the depth-averaged currents and waves to match the hourly timestep of the tides using the approx function.

We combined all data into a single dataset and applied functions from the bedshear package to calculate bed shear stress for the six habitat types.

Sediment porosity

Log-transformed porosity has been shown to have a sigmoidal relationship with log₁₀(median grain size) (D₅₀, mm) (Wilson et al. 2018):

\[log_{10}(porosity) = p_3 + p_4\left(\frac{1}{1+e^{(\frac{-log_{10}(D_{50})-p_1}{p_2})}}\right)\]

We use this relationship to calculate porosity for seabed sediments in the Ascension Island (Table 2), using an alternative parameterisation to Wilson (Pace et al. 2021). This alternative set of parameters extends the relationship to fine, muddy sediments (Table 3).

Table 3: The four parameters for the function relating sediment porosity to median grain size. From Pace et al. (in review)

Hydraulic conductivity

Hydraulic conductivity (H, m.s^-1) represents the ease with which fluids flow through the particle grain matrix. The related term ‘permeability’ (m^-2) is a measure of the connectedness of the fluid filled void spaces between the particle grains. Permeability is a function only of the sediment matrix, whilst conductivity is a function of both the sediment and the permeating fluid, in particular the fluid viscosity and density. Hydraulic conductivity is related to permeability by:

\[H = Permeability∙fluid\;density∙\frac{𝑔}{dynamic\;viscosity}\] where: seawater density = 1027 kg.m^-3 at salinity 35 and temperature 10°C; seawater dynamic viscosity = 1.48 x 10^-3 kg.m^-1.s^-1 at salinity 35 and temperature 10°C; g = acceleration due to gravity = 9.8 m.s^-1

Hence, \(H = Permeability · 6.8004·10^6\) (m.s^-1 at salinity 35 and temperature 10°C)

Whole sediment permeability can be related to the proportion of sediment classed as mud (D₅₀ < 62 μm) (Pace et al. 2021). In this model implementation, this relationship is utilized for internal calculations.

Sediment organic nitrogen content

The magnitude of the static (refactory) organic nitrogen detritus pool in each sediment type is a required input to the model. The code includes an option to impute values from empirical relationships between total organic nitrogen (TON) and mud content, and between mud content and median grain size. This relationship has been documented in the North Sea implementation (Heath et al. 2021). For the Ascension Island implementation, the total nitrogen content is internally calculated and based on that relationship.

Configuration parameters

Event timing parameters (not fitted)

Event timing parameters (fitted)

Tunas that forage around Ascension Island probably migrate to other areas to breed (Richardson et al., 2018). Bigeye and yellowfin tuna primarily spawn in tropical waters, such as the Gulf of Guinea and the Gulf of Mexico, during the summer (Carleton et al., 2010). Due to the highly migratory nature of tunas, assessment of local population size and trends is challenging (Ascension Island Government, 2016). However, tagging studies have indicated local site fidelity of yellowfin tuna, with extended periods of residency around oceanic islands (Ascension Island Government, 2016; Richardson et al., 2018). Tagging of bigeye tuna around seamounts has also suggested some degree of site fidelity, with at least short-term residency around these features (Ascension Island Government, 2016). Atlantic blue marlin (Makaira nigricans) and Atlantic sailfish (Istiophorus albicans) have been tagged and tracked near Ascension as well, where tend to disperse out of the EEZ over several weeks, migrating to destinations including Cape Verde, the Gulf of Guinea, and the central North Atlantic (Ascension Island Marine Protected Area, 2019).

We utilized outputs from the SEAPODYM model, employed to characterize the movement dynamics of two tuna species in the Atlantic Ocean, as part of research conducted within the Mission Atlantic project [Merillet et al. X (SOURCE)]. These outputs are based on the same four climate projections derived from NEMO-ERSEM physical outputs used in the StrathE2E driving data (see section X). The projections are driven by two SSPs (SSP3-7.0 and SSP1-2.6) and two CMIP6 models (CNRM-CM6-1-HR and GFDL-ESM4). Skipjack tuna (Katsuwonus pelamis) and albacore tuna (Thunnus alalunga) were modeled using SEAPODYM-MASS, an age-structured dynamic population model that accounts for prey availability. Historical data were provided from 1990 to 2014, along with projections from 2015 to 2070, on a monthly basis and averaged across the Ascension model domain. Data included biomass estimates for each SSP, model, and total stock biomass in the Atlantic. To establish the baseline period from 2010-2019, historical data from 2010–2014 were combined with projections from 2015–2019. The remaining time series were divided into decadal intervals from 2020–2029 to 2060–2069 for each of the four climate projections.

To identify the start and end days for annual immigration and emigration periods, we interpolated data to obtain daily values averaged by decade for each projection. To synchronize with the StrathE2E annual cycle, each month was standardized to 30 days. Seasonal cycles for each decade and projections were extracted using the decompose function in R, excluding trends and random components - for more information check the function documentation. Immigration was defined as the period during which biomass increased from the 20th to the 80th percentile, while emigration was the period during which biomass decreased from the 80th to the 20th percentile.

Figure 4: Seasonal component of migratory fish biomass derived from SEAPODYM model outputs. Points A to D indicate the start and end dates of immigration and emigration for each decade (2010–2019 to 2060–2069), under two climate scenarios (SSP1-2.6 and SSP3-7.0) and two forcing models (CNRM and GFDL).

As the migratory guild in Ascension Island includes species beyond skipjack and albacore tunas, we utilized SEAPODYM outputs to parameterize the local biomass. This was done using a scaling factor to account for the multi-species migratory biomass from the Atlantic, since local stock data are unavailable. In addition to skipjack and albacore, we considered 16 other species contributing to migratory stocks in the Atlantic that may enter Ascension EEZ, including: wahoo (Acanthocybium solandri), frigate tuna (Auxis thazard), Galapagos shark (Carcharhinus galapagensis), common dolphinfish (Coryphaena hippurus), Atlantic sailfish (Istiophorus albicans), shortfin mako (Isurus oxyrinchus), white marlin (Kajikia albida), blue marlin (Makaira nigricans), blue shark (Prionace glauca), Atlantic bonito (Sarda sarda), smooth hammerhead (Sphyrna zygaena), yellowfin tuna (Thunnus albacares), southern bluefin tuna (Thunnus maccoyii), bigeye tuna (Thunnus obesus), Atlantic bluefin tuna (Thunnus thynnus), and swordfish (Xiphias gladius).

These migratory fish stocks in the Atlantic are managed under the International Convention for the Conservation of Atlantic Tunas (ICCAT), which relies on a consensus-driven decision-making process among member nations. Biomass estimates for this study were derived from Sea Around Us (SAU) stock assessments, which utilize ICCAT data and the CMSY method (Pauly et al., 2020; see www.seaaroundus.org/cmsy-method for details). These estimates covered the period from 2010 to 2018, and we used the averaged values for our calculations. The combined biomass of the 18 key stocks in the Atlantic, which may enter the Ascension EEZ, totaled 5,503,844.45 tons. This was treated as the ‘global’ stock biomass of migratory fish, with a portion assumed to enter the model domain in all decades and projections.

The scaling factor was derived by dividing this global biomass by the skipjack and albacore stocks provided by SEAPODYM. This factor was then applied to local skipjack and albacore biomass from SEAPODYM to estimate the total migratory biomass for Ascension Island, averaged by decade, model, and SSP. To quantify the proportion of Atlantic migratory biomass entering the model domain annually, we divided the scaled local biomass by the Atlantic migratory biomass. To estimate the proportion of peak biomass that remained within the model domain without emigrating, we extracted the maximum and minimum values from the seasonal cycle.

Using these results, we derived the necessary parameters for lines 17–26 in the event_timing_ASCENSION_*.csv files, for each decade and the four climate projections.

Table 5: Biological event timing parameters related to migratory fish constant across the time periods and climate projections

Table 6: Biological event timing parameters related to migratory fish for the baseline period (2010-2019) across the four climate projections.

NEMO-ERSEM ensemble:

Four different NEMO-ERSEM runs were used to parameterise different versions of the Ascension Island implementation of StrathE2E. These four runs are a 2x2 factorial cross of two future projected scenarios (SSP126 and SSP370) with a historical hindcast from 2015, and forcing by two atmospheric models (CNRM and GFDL). In the following sections model output was processed for a 2010-2019 baseline period, and then decadal projections from 2020-2029 until 2060-2069.

Vertical mixing coefficients between the model layers:

Vertical diffusivity from the NEMO-ERSEM coupled hydro-geochemical model output was interpolated for each grid cell at the 60m boundary depth between the shallow and deep layers of the offshore zone, and the 600m boundary at the deep sea overhang. These values were summarised as monthly averages into period-specific climatological annual cycles of data for decadal periods for all combinations of SSPs and forcings.

Monthly averaged temperatures for each water column layer:

Derived by monthly averaging values at grid points within the inshore and vertical layers of the offshore zones from the NEMO-ERSEM coupled hydro-geochemical model output, weighted by grid point volumes. Values were summarised into period-specific climatological annual cycles of data.

Monthly averaged suspended particulate matter (SPM) concentrations (mg.m^-3) in the shallow zone and the deep zone upper layer:

Monthly averaged values of inorganic suspended particulate matter in sea water are available from the Globcolour project, starting from September 1997. These data are derived from satellite observations using the algorithm of Gohin (gohin11?). Data were downloaded from the ftp server (ftp://ftp.hermes.acri.fr/GLOB/merged/month/). We summarised these values as zonal statistics for the model domain to acquire a climatological annual cycle of data for the 2010-2019 simulation period only.

Monthly average light attenuation coefficients for the inshore and offshore surface layers:

Light attenuation in open water was parameterised from a linear relationship between the light attenuation coefficient and suspended particulate matter concentration (SPM) (Devlin et al., 2008).

Monthly averaged daily integrated irradiance at the sea surface (E.m^-2.d^-1):

Derived from HadGEM2-ES model output (Jones et al. 2011) which forces the NEMO-ERSEM model run used throughout our implementation. Monthly mean values were summarised into climatological annual cycles of data for decadal periods and NEMO-ERSEM ensembles.

Monthly averaged daily atmospheric deposition rates of oxidised and reduced nitrogen onto the sea surface in the shallow and deep zones (mMN.m^-2.d^-1):

Sourced from the “histsoc” files for a 1901 - 2021 hindcast as monthly averages (yang22?), available from CDS. Monthly values were summarised into climatological annual cycles of monthly oxidised and reduced nitrogen deposition rates extracted for 2010-2019. ISIMIPb Projections for SSP126 and SSP370 were processed in the same way for decadal periods up to and including the 2060s.

Monthly averaged, freshwater river inflow rates (expressed as a daily proportion of the receiving layer volume), and concentrations of oxidised and reduced dissolved inorganic nitrogen in the inflowing river waters (mMN.m^-3):

Freshwater inflow derived from HadGEM2-ES model output (Jones et al. 2011) which forces the NEMO-MEDUSA model run used throughout our implementation. Monthly values were summaries into a climatological annual cycle of data for both the 2010-2019 and 2040-2049 periods.

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Volume fluxes into the model domain across open sea boundaries, and from the upper layer of the offshore/deep zone into the inshore/shallow zone, expressed as proportions of the receiving layer volume per day:

Monthly averaged daily inflow and outflow volume fluxes derived by integrating daily mean velocities directed perpendicular to transects along the model domain boundary at grid points in each depth layer along transects through outputs from the NEMO-ERSEM coupled hydro-geochemical model output. Monthly averaged daily inflow volume fluxes then divided by the volume of the receiving layer in the model domain to estimate a daily flushing rate.

Mean concentrations of nitrate, ammonia, phytoplankton and suspended detritus (mMN.m^-3), in adjacent ocean waters inflowing to the offshore/deep zone upper layer, adjacent ocean waters inflowing to the offshore/deep zone lower layer, and adjacent shelf waters inflowing to the inshore/shallow zone:

NEMO-ERSEM outputs included nitrate, ammonia, phytoplankton and suspended detritus. We calculated the depth-averaged concentrations for pixels within the shallow and deep layers of StrathE2E. We then sampled the pixels using the same transects around the model domain as for sampling volume fluxes. Only transects where water flowed into the model domain were sampled, and the average concentration of inflowing waters for target variables was calculated weighting by the flow rate across a transect and the cross-sectional area represented by a transect (average depth and length). Concentrations were then averaged into climatological annual cycles. The flow weighted averaging of concentrations was calculated on the 5-day NEMO-ERSEM timestep, before averaging to a monthly climatology of concentrations.

Background

The key configuration data for the fishing fleet model are the definitions of the gears in terms of their power with respect to each of the harvestable resource guilds, discards and bycatch rates, processing-at-sea rates, and seabed abrasion rates. These can be regarded as static parameters for each fleet. Fleets were defined based on available data from databases and literature, with some grouped according to similarities and potential Ascension Island EEZ policy and management scenarios. Species were categorized into the commercial guilds based on ecological properties using information from databases such as FishBase (Froese and Pauly 1999; https://www.fishbase.se/).

Historically, Ascension faced considerable fishing pressure, primarily from foreign longline fleets targeting pelagic species such as tuna and sharks (Reeves and Laptikhovsky, 2014). Although commercial longlining has declined, recreational and subsistence fishing remain popular activities within the Territory, providing a crucial source of food for the community (Ascension Island Government, 2015).

After prohibitions, commercial fisheries were reopened in 2010, prohibited again across the entire EEZ in 2014, and then partially reopened in 2015 under updated regulations that allowed fishing in the northern half of the EEZ while the southern half remained a closed area within 50 nautical miles of the island (Ascension Island Government, 2018; Rowlands et al., 2019). However, between 2015 and 2018, the uptake of licenses was minimal, with only one or two licenses issued annually (Ascension Island Government, 2019). In 2019, the Ascension Island MPA was designated, covering 100% of Ascension’s EEZ, making it one of the largest MPAs in the world. The MPA prohibits fishing beyond 12 nautical miles from the island and bans large-scale commercial fishing throughout its 443,000 km² area, which includes Ascension’s three shallow water seamounts (Ascension Island Government, 2021).

Commercial longlining has been licensed in the Ascension Island EEZ since 1988, mainly with Taiwanese and Japanese and others foreign-flagged vessels targeting bigeye tuna (Thunnus obesus), yellowfin tuna (Thunnus albacares), swordfish (Xiphias gladius), blue marlin (Makaira nigricans), and sharks (Irving, 2015; Ascension Island Government, 2019).

In addition to the licensed and regulated fishery, illegal, unreported, and unregulated (IUU) fishing for tuna and other species in the Atlantic is a concern, with some vessels potentially crossing the Ascensio EEZ. Satellite surveillance, coordinated by the Blue Belt Surveillance and Intelligence Hub in the UK, is used to detect such illegal activities (The Fisheries Conservation and Management Ordinance, 2015). For the baseline period, we included potential fishing catches from purse-seine fleets, primarily targeting yellowfin and other tunas.

On a smaller scale, subsistence and recreational fishing are practiced for the local community. Fishing methods include mainly handline, but also rod and line and spearfishing (Ascension Island Government, 2019). In the sublittoral zone, these small-scale shore-based and vessel-based angling and spearfishing target reef fish such as rock hind (Epinephelus ascensionis), spotted moray (Gymnothorax moringa), and glasseye snapper (Heteropriacanthus cruentatus) (Ascension Island Government, 2015). The recreational fishery for spiny lobsters (Panulirus echinatus) is highly valued but remains unquantified, posing a significant threat to local stocks (Ascension Island Government, 2015b). Additionally, common octopuses (Octopus occidentalis) are caught in the local fishery and used as bait (Ascension Island Inshore Fisheries Strategy, 2022).

Based on this information, we grouped the following fleets in the fishing model: commercial purse-seine, longlines, and otter trawl, along with inshore artisanal handlines and hand-gathering and pots. IUU purse-seine and longline fleets were included in the baseline scenario without specific values to simulate their potential impact.

Details regarding the sources used for landings, discards, bycatch, effort and other fishing data compilation is described below.

Figure 5: Fishing fleets defined for the StrathE2E Ascension Island EEZ model, their operational descriptions, target-species and discards levels.

Landings

Hand-gathering and pots

Hand-gathering of spiny lobster and octopus is also practiced by a smaller number of individuals, and we included this fleet in the model (Ascension Island Inshore Fisheries Strategy, 2022). Although data were unavailable, we used reported values from St. Helena as a proxy for Ascension, estimating 0.046 tons annually (St. Helena Government, 2016).

Figure 6: Catches per year and per fleet compiled for the Ascension Island EEZ StrathE2E model.

Discards

Discard rates (the proportion of the catch discarded) were calculated using data from the SAU database, filtered for the period between 2010 and 2019, and allocated to StrathE2E guilds. Guild-specific discard rates were derived by calculating discard ratios and applying these to the landing values mentioned in section X.

For the artisanal handline fleet, we assumed a zero discard rate as no discard data were recorded in the SAU database. This assumption is supported by the Ascension Island Government (2015) report, which indicates that discards of non-target species in the shallow sub-littoral fishery are minimal, and by Kelleher (2005), who noted that discard rates in such fisheries are typically low or negligible.

The longline fleet exhibited a discard rate of 19.47% for the Migratory guild, with unidentified species averaging 289 tons per year between. This rate is slightly higher than the 15% reported by Freire et al. (2014) for pelagic longlines and the FAO global database (Kelleher, 2005), which notes that tropical longliners frequently retain sharks and other non-target species. Observations on Taiwanese tuna longline vessels, as reported by Irving (2015), indicated that bycatch accounted for 35% of the total tuna catch. The bycatch primarily consisted of blue sharks (52%), with other shark species making up 11%, and various fish species contributing the remaining 37%. According to Reeves and Laptikhovsky (2014), blue sharks are likely a significant part of the discards within the Ascension EEZ. Other migratory shark species frequently discarded by commercial longlining vessels include Galapagos sharks (Carcharhinus galapagensis), bigeye thresher sharks (Alopias superciliosus), and oceanic whitetip sharks (Carcharhinus longimanus) (Irving, 2015).

For the purse-seine fleet, the discard rate was 4.7%, closely aligning with the 4.1% reported in the FAO global database for Atlantic purse-seine fleets targeting tuna. The otter trawl fleet had a discard rate of 12.28%, attributed to a high discard value in 2018 reported in the SAU database. We assumed no discards for the hand-gathering and pots fleet targeting lobsters.

Bycatch

Although seabird bycatch has not been reported in any UK Overseas Territories recently (ICCAT, 2022), species such as boobies, noddies, and diving petrels do associate with fishing vessels, and the impact of bycatch on seabirds is classified as ‘very low,’ though not negligible, within the Ascension Island EEZ (Reeves and Laptikhovsky, 2014). According to the Ascension Island Government report (2015), incidental capture of Ascension frigatebirds (Fregata aquila) in both shore-based and vessel-based recreational fisheries occurs periodically and can result in drowning or fatal injury. The report indicates that the number of affected individuals is currently estimated to be low, but it could become a concern if local fishing effort were to increase significantly.

Due to the lack of specific bycatch studies in the Ascension region, we extrapolated seabird bycatch rates from similar fleets from another island in the SOMAR region, the Saint Peter and Saint Paul Archipelago (see X model implementation). We applied a mortality rate of 0.05 birds per day for longline fleets and 0.143 birds per day for handlines. These daily bird mortality rates were multiplied by the estimated fishing days for each fleet (see Section X.Y for fishing effort). To estimate bycatch in terms of biomass, we used the average weight of these species as reported in the literature. The total bycatch from both fleets was estimated at approximately 98 individual birds, equivalent to around 0.1 tonnes per year. This data was converted to mMN.m⁻².y⁻¹ using the appropriate conversion factor (see Greenstreet et al., 1996, Table X).

It is important to note that the methodology developed here is a simplification, designed only to parameterize the Ascension EEZ StrathE2E model. The annual bycatch data presented should not be regarded as a comprehensive assessment of bycatch in the region.

Fishing effort and distribution

We examined the apparent fishing hours of vessels equipped with satellite tracking devices under the Global Fishing Watch (GFW) program, available from 2012 onwards. However, during the baseline period, the data averaged only 104 hours per year within the Ascension Island EEZ, which appears to be very underestimated when compared to the annual catches. Given that many vessels may not be registered with GFW in the baseline period, we decided not to use this data source. Nevertheless, we found that the majority of the recorded fishing activity involved “drifting longlines” from vessels flagged by Taiwan, Japan, Spain, and the UK, which aligns with the information reported in Section X.

For the longline fleet, effort data in the ICCAT T2CE database is recorded within 5ºx5º geographical grid cells (Task 2 – Catch and Effort; see Coulter et al., 2019 for details on fleet allocation procedures). We identified the central points of these grid cells and filtered the data that fell within the Ascension EEZ domain. The data included the number of hooks used, which averaged 6,999,154 annually. We made a rough approximation, assuming 3.00 hooks per set and an average of 12 hours per set, as described by Huang (2013) for Taiwanese vessels targeting bigeye tuna. Using this approach, we estimated an annual CPUE (Catch Per Unit Effort), which was then applied to the reported catches in Section X for each year. This resulted in an average of 18,443 fishing hours per year, with a peak in 2011 of 63,371.04 hours. No effort was considered in 2015, 2017, 2018, and 2019 since the absence of catches. In reference X, approximately 10,700 fishing days are reported within the Ascension EEZ from 2011 to 2013, which, assuming 12 hours of fishing per day, equates to 128,400 hours. Our analysis using ICCAT data yielded a similar figure of 126,005.17 fishing hours for the same period, indicating a close correspondence.

There were no available indicators of fishing effort directed at the purse-seine fleet within the Ascension EEZ. Therefore, we used estimates from another island in the SOMAR region, the Saint Peter and Saint Paul Archipelago. In the model implementation (see X source), we considered a CPUE of approximately 0.028 tons per hour. This CPUE was applied to our catch data to calculate the average annual effort.

Similarly, to our knowledge, there were no available indicators of fishing effort directed at the artisanal handline fleet within the Ascension Island EEZ. We based our estimates on the effort primarily targeting grouper, as reported in the Blue Belt Programme for the Saint Helena EEZ (Riley et al., 2020) for the period from 2010 to 2019, estimating a CPUE of 0.0182 tons per hour. This CPUE was applied to our catch data to calculate the average annual effort.

For lobster fishing, we relied on estimates from another island in the SOMAR region, the Saint Peter and Saint Paul Archipelago, applying a CPUE of 0.014 tons per hour, as no specific data were available for the Ascension Island EEZ.

The final unit for input into the model for each fleet was sec.m^-2.d^-1 (Table 7).

Table 8: Effort compiled per fleet for the baseline period implementation of Ascension Island EEZ model

Seabed abrasion

For most fleets, we assumed no seabed abrasion (ploughing) rate per unit of time, as they primarily operate in pelagic zones. However, an exception was made for the Otter Trawl fleet, for which we applied a seabed abrasion rate of 17.1 m²·s⁻¹, based on data from Eigaard et al. (2015).

Other processing

1. Total catch by fleet and guild was calculated by combining landings with discard/bycatch values.

2. Discarded weight was calculated as catch - landings.

3. When catch was 0 discard rates were set to 1.

4. Fishing power was calculated as catch / activity per fleet.

Production and PB ratios

Calculating the production involved compiling and adjusting biomass values for the model area and converting them to molar nitrogen mass following Greenstreet (1996). Subsequently, convert biomass into gross production using PB ratios. For top-predators, net production was calculated using the relationship net production = 0.6 * gross production.

In our literature search, we encountered a limited number of recent studies quantifying biomass in the Ascension Island EEZ, as described below. Details regarding the sources of biomass and PB ratios, along with the calculated values, are available in Table 10.

Table 10: PB ratios utilized to estimate production as target data for each commercial guild in the model. Production values are presented in tonnes. The mMN conversion indicates the values used to convert the gross and net production from grams to molar nitrogen mass, using Greenstreet (1996). The final production is presented in units of millimoles of nitrogen per m².

Diet proportions

We reviewed the dietary preferences of the main seabirds species occurring in Ascension Island (see Section X). The dietary proportions identified were subsequently utilized as target data for the fitting process. We then calibrated the resource-consumer matrix of the model using a simulated annealing scheme to align the 2010-2019 model with observed ecosystem state data. Although we did not include the dietary proportions of cetaceans due to the absence of specific local studies, the species occurring around Ascension are known to consume prey in other regions that are already represented in the base model preference matrix.

Harverting ratio

To derive the regional harvest ratio, we calculated the harvest ratio for each commercial guild by dividing the annual catches (landings + discards) of each guild by the biomass of the compiled commercial species (refer to the catch methodology on page X and biomass methodology on page Y). For predator guilds, the annual bycatch value was used instead of the catch value. The input unit for StrathE2E is the harvesting ratio per day.

This provided sufficient information to calculate initial values for the scaling parameters linking effort to harvest ratios using the function e2e_calculate_hrscale(). We estimated the effort-harvest ratio scaling parameters required to produce the best fit to the 2010-2019 target data using the function e2e_optimize_hr(). This optimization was applied exclusively to the Migratory fish, Demersal fish and Seabirds guilds, as the fishing model does not include the Planktivorous fish, DF benthos and CS zooplankton, and for the CS benthos guild we lacked the necessary biomass data to perform these calculations.

Ambiental data

Chlorophyll concentration was used as a target indicator for primary productivity. Monthly surface chlorophyll concentrations (mg/m³) were obtained from the AQUA-MODIS satellite data, with a resolution of 1 km, covering the entire Ascension Island EEZ domain and representing the average values for the period from 2010 to 2019.

Acknowledgements

Financial support for the development of the Ascension Island implementation of StrathE2E2 came from the European Union’s Horizon 2020 research and innovation programme (Mission Atlantic - No. 862428). We are grateful to:

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Appendix

The species mentioned in this documentation and/or in the data used in the construction of the model are in Table 12 by guild of the StrathE2E model.

Table 12: List of the main species considered per StrathE2E guild in the Ascension Island EEZ model.

{r list_species, fig.cap = tab_list_cap}

References

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